Methods for compression of molecular tagged nucleic acid sequence data

ABSTRACT

A method for compressing molecular tagged sequence data includes: grouping sequence reads associated with a molecular tag sequence to form a family of sequence reads, corresponding vectors of flow space signal measurements and corresponding sequence alignments, calculating an arithmetic mean of the corresponding vectors of flow space signal measurements to form a vector of consensus flow space signal measurements, calculating a standard deviation of the corresponding vectors of flow space signal measurements to form a vector of standard deviations, determining a consensus base sequence based on the vector of consensus flow space signal measurements, determining a consensus sequence alignment and generating a compressed data structure comprising consensus compressed data, the consensus compressed data including for each family, the consensus base sequence, the consensus sequence alignment, the vector of consensus flow space signal measurements, the vector of standard deviations and the number of members.

CROSS-REFERENCE

This application is a continuation of U.S. application Ser. No.15/979,804 filed May 15, 2018, which claims priority to U.S. applicationNo. 62/507,117 filed May 16, 2017, and U.S. application No. 62/517,235filed Jun. 9, 2017, which disclosures are herein incorporated byreference in their entirety.

BACKGROUND OF THE INVENTION

Molecular tagging of nucleic acid sequences is useful for identifyingthe nucleic acid sequence reads that originate from the samepolynucleotide molecule and classifying them into a family based ontheir tag sequence. Large amounts of molecular tagged nucleic acidsequence data, obtained from a nucleic acid sample using varioustechniques, platforms or technologies, may be stored and processed forvariant calling. There is a need for new methods, systems and computerreadable media to compress nucleic acid sequence data to reduce memoryrequirements for storage and improve computational efficiency forvariant calling operations, without compromising quality in variantcalling.

BRIEF SUMMARY OF THE INVENTION

According to an exemplary embodiment, there is provided a method forcompressing molecular tagged nucleic acid sequence data, comprising (a)receiving a plurality of nucleic acid sequence reads, a plurality ofvectors of flow space signal measurements and a plurality of sequencealignments, wherein each sequence read is associated with a moleculartag sequence, the molecular tag sequence identifying a family ofsequence reads resulting from a particular polynucleotide molecule in anucleic acid sample, wherein each vector of flow space signalmeasurements and each sequence alignment correspond with one of thesequence reads; (b) grouping sequence reads associated with a samemolecular tag sequence to form a family of sequence reads, correspondingvectors of flow space signal measurements and corresponding sequencealignments, each family having a number of members; (c) calculating anarithmetic mean of the corresponding vectors of flow space signalmeasurements to form a vector of consensus flow space signalmeasurements for the family; (d) calculating a standard deviation of thecorresponding vectors of flow space signal measurements to form a vectorof standard deviations for the family; (e) determining a consensus basesequence based on the vector of consensus flow space signal measurementsfor the family; (f) determining a consensus sequence alignment bycomparing the consensus base sequence to the sequence read having ahighest mapping quality of the corresponding sequence alignments for thefamily; and (g) generating a compressed data structure comprisingconsensus compressed data, the consensus compressed data including foreach family, the consensus base sequence, the consensus sequencealignment, the vector of consensus flow space signal measurements, thevector of standard deviations and the number of members.

According to an exemplary embodiment, there is provided a non-transitorymachine-readable storage medium comprising instructions which, whenexecuted by a processor, cause the processor to perform a method forcompressing molecular tagged nucleic acid sequence data, comprising:comprising (a) receiving a plurality of nucleic acid sequence reads, aplurality of vectors of flow space signal measurements and a pluralityof sequence alignments, wherein each sequence read is associated with amolecular tag sequence, the molecular tag sequence identifying a familyof sequence reads resulting from a particular polynucleotide molecule ina nucleic acid sample, wherein each vector of flow space signalmeasurements and each sequence alignment correspond with one of thesequence reads; (b) grouping sequence reads associated with a samemolecular tag sequence to form a family of sequence reads, correspondingvectors of flow space signal measurements and corresponding sequencealignments, each family having a number of members; (c) calculating anarithmetic mean of the corresponding vectors of flow space signalmeasurements to form a vector of consensus flow space signalmeasurements for the family; (d) calculating a standard deviation of thecorresponding vectors of flow space signal measurements to form a vectorof standard deviations for the family; (e) determining a consensus basesequence based on the vector of consensus flow space signal measurementsfor the family; (f) determining a consensus sequence alignment bycomparing the consensus base sequence to the sequence read having ahighest mapping quality of the corresponding sequence alignments for thefamily; and (g) generating a compressed data structure comprisingconsensus compressed data, the consensus compressed data including foreach family, the consensus base sequence, the consensus sequencealignment, the vector of consensus flow space signal measurements, thevector of standard deviations and the number of members.

According to an exemplary embodiment, there is provided a system forcompressing molecular tagged nucleic acid sequence data, including: amachine-readable memory and a processor configured to executemachine-readable instructions, which, when executed by the processor,cause the system to perform a method for compressing molecular taggednucleic acid sequence data, comprising: comprising (a) receiving aplurality of nucleic acid sequence reads, a plurality of vectors of flowspace signal measurements and a plurality of sequence alignments,wherein each sequence read is associated with a molecular tag sequence,the molecular tag sequence identifying a family of sequence readsresulting from a particular polynucleotide molecule in a nucleic acidsample, wherein each vector of flow space signal measurements and eachsequence alignment correspond with one of the sequence reads; (b)grouping sequence reads associated with a same molecular tag sequence toform a family of sequence reads, corresponding vectors of flow spacesignal measurements and corresponding sequence alignments, each familyhaving a number of members; (c) calculating an arithmetic mean of thecorresponding vectors of flow space signal measurements to form a vectorof consensus flow space signal measurements for the family; (d)calculating a standard deviation of the corresponding vectors of flowspace signal measurements to form a vector of standard deviations forthe family; (e) determining a consensus base sequence based on thevector of consensus flow space signal measurements for the family; (f)determining a consensus sequence alignment by comparing the consensusbase sequence to the sequence read having a highest mapping quality ofthe corresponding sequence alignments for the family; and (g) generatinga compressed data structure comprising consensus compressed data, theconsensus compressed data including for each family, the consensus basesequence, the consensus sequence alignment, the vector of consensus flowspace signal measurements, the vector of standard deviations and thenumber of members.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings of which:

FIG. 1 illustrates an example of molecular tagging where uniquemolecular tags have been attached to individual polynucleotide moleculesfollowed by PCR amplification and sequencing.

FIG. 2 is a block diagram of an exemplary method for generatingconsensus compressed data, in accordance with an embodiment.

FIG. 3 is a block diagram of an exemplary method for the consensuspipeline 206, in accordance with an embodiment.

FIG. 4 shows an exemplary representation of flow space signalmeasurements from which base calls may be made.

FIG. 5 illustrates an exemplary plot of flow space signal measurementsfor a single family.

FIG. 6 illustrates an exemplary plot of consensus flow space signalmeasurements for a single family.

FIG. 7 is a block diagram of an exemplary method of using consensuscompressed data for variant calling operations.

FIG. 8 illustrates an example of a posteriori probabilities APP(H_(REF))and APP(H_(VAR)) found by the integrations of equations (9) and (10).

FIG. 9A shows a plot of an exemplary comparison of quality values, QUAL,of variants called using the original data from the mapped BAM fileversus consensus compressed data from the mapped consensus BAM file.

FIG. 9B shows a plot of an exemplary comparison of radius of bias (RBI),estimated using the original data from the mapped BAM file versusconsensus compressed data from the mapped consensus BAM file.

FIG. 9C shows a plot of an exemplary comparison of allele frequencies(AF), of variants called using the original data from the BAM fileversus consensus compressed data from the consensus BAM file.

FIG. 10 is a block diagram of an exemplary system for nucleic acidsequencing, in accordance with an embodiment.

DETAILED DESCRIPTION OF THE INVENTION

In accordance with the teachings and principles embodied in thisapplication, new methods, systems and non-transitory machine-readablestorage medium are provided to compress molecular tagged nucleic acidsequence data to form consensus compressed data for families of nucleicacid sequence reads associated with unique molecular tags. Furtherteachings provide for detecting variants based on the consensuscompressed data.

In various embodiments, DNA (deoxyribonucleic acid) may be referred toas a chain of nucleotides consisting of 4 types of nucleotides; A(adenine), T (thymine), C (cytosine), and G (guanine), and that RNA(ribonucleic acid) is comprised of 4 types of nucleotides; A, U(uracil), G, and C. Certain pairs of nucleotides specifically bind toone another in a complementary fashion (called complementary basepairing). That is, adenine (A) pairs with thymine (T) (in the case ofRNA, however, adenine (A) pairs with uracil (U)), and cytosine (C) pairswith guanine (G). When a first nucleic acid strand binds to a secondnucleic acid strand made up of nucleotides that are complementary tothose in the first strand, the two strands bind to form a double strand.In various embodiments, “nucleic acid sequencing data,” “nucleic acidsequencing information,” “nucleic acid sequence,” “genomic sequence,”“genetic sequence,” or “fragment sequence,” or “nucleic acid sequencingread” denotes any information or data that is indicative of the order ofthe nucleotide bases (e.g., adenine, guanine, cytosine, andthymine/uracil) in a molecule (e.g., whole genome, whole transcriptome,exome, oligonucleotide, polynucleotide, fragment, etc.) of DNA or RNA.

In various embodiments, a “polynucleotide”, “nucleic acid”, or“oligonucleotide” refers to a linear polymer of nucleosides (includingdeoxyribonucleosides, ribonucleosides, or analogs thereof) joined byinternucleosidic linkages. Typically, a polynucleotide comprises atleast three nucleosides. Usually oligonucleotides range in size from afew monomeric units, e.g. 3-4, to several hundreds of monomeric units.Whenever a polynucleotide such as an oligonucleotide is represented by asequence of letters, such as “ATGCCTG,” it will be understood that thenucleotides are in 5′->3′ order from left to right and that “A” denotesdeoxyadenosine, “C” denotes deoxycytidine, “G” denotes deoxyguanosine,and “T” denotes thymidine, unless otherwise noted. The letters A, C, G,and T may be used to refer to the bases themselves, to nucleosides, orto nucleotides comprising the bases, as is standard in the art.

The term “allele” as used herein refers to a genetic variationassociated with a gene or a segment of DNA, i.e., one of two or morealternate forms of a DNA sequence occupying the same locus.

The term “locus” as used herein refers to a specific position on achromosome or a nucleic acid molecule. Alleles of a locus are located atidentical sites on homologous chromosomes.

As used herein, the terms “adapter” or “adapter and its complements” andtheir derivatives, refers to any linear oligonucleotide which can beligated to a nucleic acid molecule of the disclosure. Optionally, theadapter includes a nucleic acid sequence that is not substantiallycomplementary to the 3′ end or the 5′ end of at least one targetsequences within the sample. In some embodiments, the adapter issubstantially non-complementary to the 3′ end or the 5′ end of anytarget sequence present in the sample. In some embodiments, the adapterincludes any single stranded or double-stranded linear oligonucleotidethat is not substantially complementary to an amplified target sequence.In some embodiments, the adapter is substantially non-complementary toat least one, some or all of the nucleic acid molecules of the sample.In some embodiments, suitable adapter lengths are in the range of about10-100 nucleotides, about 12-60 nucleotides and about 15-50 nucleotidesin length. An adapter can include any combination of nucleotides and/ornucleic acids. In some aspects, the adapter can include one or morecleavable groups at one or more locations. In another aspect, theadapter can include a sequence that is substantially identical, orsubstantially complementary, to at least a portion of a primer, forexample a universal primer. In some embodiments, the adapter can includea barcode or tag to assist with downstream cataloguing, identificationor sequencing. In some embodiments, a single-stranded adapter can act asa substrate for amplification when ligated to an amplified targetsequence, particularly in the presence of a polymerase and dNTPs undersuitable temperature and pH.

As used herein, “DNA barcode” or “DNA tagging sequence” and itsderivatives, refers to a unique short (e.g., 6-14 nucleotide) nucleicacid sequence within an adapter that can act as a ‘key’ to distinguishor separate a plurality of amplified target sequences in a sample. Forthe purposes of this disclosure, a DNA barcode or DNA tagging sequencecan be incorporated into the nucleotide sequence of an adapter.

In some embodiments, the disclosure provides for amplification ofmultiple target-specific sequences from a population of target nucleicacid molecules. In some embodiments, the method comprises hybridizingone or more target-specific primer pairs to the target sequence,extending a first primer of the primer pair, denaturing the extendedfirst primer product from the population of nucleic acid molecules,hybridizing to the extended first primer product the second primer ofthe primer pair, extending the second primer to form a double strandedproduct, and digesting the target-specific primer pair away from thedouble stranded product to generate a plurality of amplified targetsequences. In some embodiments, the digesting includes partial digestingof one or more of the target-specific primers from the amplified targetsequence. In some embodiments, the amplified target sequences can beligated to one or more adapters. In some embodiments, adapters caninclude one or more DNA barcodes or tagging sequences. In someembodiments, amplified target sequences once ligated to an adapter canundergo a nick translation reaction and/or further amplification togenerate a library of adapter-ligated amplified target sequences.

In some embodiments, the methods of the disclosure include selectivelyamplifying target sequences in a sample containing a plurality ofnucleic acid molecules and ligating the amplified target sequences to atleast one adapter and/or barcode. Adapters and barcodes for use inmolecular biology library preparation techniques are well known to thoseof skill in the art. The definitions of adapters and barcodes as usedherein are consistent with the terms used in the art. For example, theuse of barcodes allows for the detection and analysis of multiplesamples, sources, tissues or populations of nucleic acid molecules permultiplex reaction. A barcoded and amplified target sequence contains aunique nucleic acid sequence, typically a short 6-15 nucleotidesequence, that identifies and distinguishes one amplified nucleic acidmolecule from another amplified nucleic acid molecule, even when bothnucleic acid molecules minus the barcode contain the same nucleic acidsequence. The use of adapters allows for the amplification of eachamplified nucleic acid molecule in a uniformed manner and helps reducestrand bias. Adapters can include universal adapters or proprietyadapters both of which can be used downstream to perform one or moredistinct functions. For example, amplified target sequences prepared bythe methods disclosed herein can be ligated to an adapter that may beused downstream as a platform for clonal amplification. The adapter canfunction as a template strand for subsequent amplification using asecond set of primers and therefore allows universal amplification ofthe adapter-ligated amplified target sequence. In some embodiments,selective amplification of target nucleic acids to generate a pool ofamplicons can further comprise ligating one or more barcodes and/oradapters to an amplified target sequence. The ability to incorporatebarcodes enhances sample throughput and allows for analysis of multiplesamples or sources of material concurrently.

In this application, “reaction confinement region” generally refers toany region in which a reaction may be confined and includes, forexample, a “reaction chamber,” a “well,” and a “microwell” (each ofwhich may be used interchangeably). A reaction confinement region mayinclude a region in which a physical or chemical attribute of a solidsubstrate can permit the localization of a reaction of interest, and adiscrete region of a surface of a substrate that can specifically bindan analyte of interest (such as a discrete region with oligonucleotidesor antibodies covalently linked to such surface), for example. Reactionconfinement regions may be hollow or have well-defined shapes andvolumes, which may be manufactured into a substrate. These latter typesof reaction confinement regions are referred to herein as microwells orreaction chambers, and may be fabricated using any suitablemicrofabrication techniques. Reaction confinement regions may also besubstantially flat areas on a substrate without wells, for example.

A plurality of defined spaces or reaction confinement regions may bearranged in an array, and each defined space or reaction confinementregions may be in electrical communication with at least one sensor toallow detection or measurement of one or more detectable or measurableparameter or characteristics. This array is referred to herein as asensor array. The sensors may convert changes in the presence,concentration, or amounts of reaction by-products (or changes in ioniccharacter of reactants) into an output signal, which may be registeredelectronically, for example, as a change in a voltage level or a currentlevel which, in turn, may be processed to extract information about achemical reaction or desired association event, for example, anucleotide incorporation event. The sensors may include at least onechemically sensitive field effect transistor (“chemFET”) that can beconfigured to generate at least one output signal related to a propertyof a chemical reaction or target analyte of interest in proximitythereof. Such properties can include a concentration (or a change inconcentration) of a reactant, product or by-product, or a value of aphysical property (or a change in such value), such as an ionconcentration. An initial measurement or interrogation of a pH for adefined space or reaction confinement regions, for example, may berepresented as an electrical signal or a voltage, which may bedigitalized (e.g., converted to a digital representation of theelectrical signal or the voltage). Any of these measurements andrepresentations may be considered raw data or a raw signal.

In various embodiments, the phrase “base space” refers to arepresentation of the sequence of nucleotides. The phrase “flow space”refers to a representation of the incorporation event ornon-incorporation event for a particular nucleotide flow. For example,flow space can be a series of values representing a nucleotideincorporation event (such as a one, “1”) or a non-incorporation event(such as a zero, “0”) for that particular nucleotide flow. Nucleotideflows having a non-incorporation event can be referred to as emptyflows, and nucleotide flows having a nucleotide incorporation event canbe referred to as positive flows. It should be understood that zeros andones are convenient representations of a non-incorporation event and anucleotide incorporation event; however, any other symbol or designationcould be used alternatively to represent and/or identify these eventsand non-events. In particular, when multiple nucleotides areincorporated at a given position, such as for a homopolymer stretch, thevalue can be proportional to the number of nucleotide incorporationevents and thus the length of the homopolymer stretch.

FIG. 1 illustrates an example of molecular tagging wherein individualpolynucleotide molecules have been labeled with unique molecular tags(UMT), amplified in a PCR reaction and sequenced. This examplerepresents the molecular tagging of two tumor variant polynucleotidemolecules and three wild type polynucleotide molecules that may beobtained from a cell free DNA (cfDNA) sample. The tumor variantpolynucleotide molecule 102 is represented as having a true variant. Thewild type polynucleotide molecule 112 does not have the variant. Uniquemolecular tags attached to each polynucleotide molecule are representedby different patterns for prefix tags 106 and 116 and suffix tags 108and 118. PCR amplification and sequencing of the tagged tumor variantpolynucleotide molecules 104 and tagged wild type polynucleotidemolecules 114 may produce multiple amplicons resulting in multiplesequence reads per original tagged polynucleotide molecule. The uniquemolecular tag is used to identify the sequence reads that originate fromthe same polynucleotide molecule and classify them into families havingthe same tag sequence. The tumor variant group of sequence reads 110shows two families with three amplicons per family, where amplicons ineach family share the same unique molecular tag sequences. The wild typegroup of sequence reads 120 shows three families, where amplicons sharethe same unique molecular tag sequences, having sizes of four, five andthree amplicons, respectively. The number and sizes of families are forillustrative purposes only and are not limiting. The tumor variantsequence reads 110 and wild type sequence reads 120 have errors depictedby X's. These errors may be due to PCR amplification errors orsequencing errors. The errors may be distributed randomly over thesequence reads. In contrast, the true variant should appear in allsequence reads associated with the polynucleotide molecule having thevariant.

A family, or molecular family, refers the set of sequence reads havingthe same unique molecular tags. The family size is the number ofsequence reads in the family. A functional family is a family that has anumber of members that is greater than a minimum family size. Theminimum family size can be any integer value. For example, the minimumfamily size can be three or greater.

FIG. 2 is a block diagram of an exemplary method for generatingconsensus compressed data, in accordance with an embodiment. Flow spacesignal measurements may be provided to a processor by a nucleic acidsequencing device. In some embodiments, each flow space signalmeasurement represents a signal amplitude or intensity measured inresponse to an incorporation or non-incorporation of a flowed nucleotideby sample nucleic acids in microwells of a sensor array. For anincorporation event, the signal amplitudes depend on the number of basesincorporated at one flow. For homopolymers, the signal amplitudesincrease with increasing homopolymer length. The processor may apply abase caller 202 to generate base calls for a sequence read by analyzingflow space signal measurements.

FIG. 4 shows an exemplary representation of flow space signalmeasurements from which base calls may be made. In this example, thex-axis shows the flow number and nucleotide that was flowed in a flowsequence. The bars in the graph show the amplitudes of the flow spacesignal measurements for each flow from a particular location of amicrowell in the sensor array. The flow space signal measurements may beraw acquisition data or data having been processed, such as, e.g., byscaling, background filtering, normalization, correction for signaldecay, and/or correction for phase errors or effects, etc. The basecalls may be made by analyzing any suitable signal characteristics(e.g., signal amplitude or intensity). The structure and/or design of asensor array, signal processing and base calling for use with thepresent teachings may include one or more features described in U.S.Pat. Appl. Publ. No. 2013/0090860, Apr. 11, 2013, incorporated byreference herein in its entirety.

Once the base sequence for the sequence read is determined, the sequencereads may be provided to mapper 204, for example, in an unmapped BAMfile. The mapper 204 aligns the sequence reads to a reference genome todetermine aligned sequence reads and associated mapping qualityparameters. Methods for aligning sequence reads for use with the presentteachings may include one or more features described in U.S. Pat. Appl.Publ. No. 2012/0197623, published Aug. 2, 2012, incorporated byreference herein in its entirety. The aligned sequence reads may beprovided to the consensus pipeline 206, for example, in a mapped BAMfile.

The BAM file format structure is described in “Sequence Alignment/MapFormat Specification,” Sep. 12, 2014(www.github.com/samtools/hts-specs), referred to as “BAM specification”herein. As described herein, a “BAM file” refers to a file compatiblewith the BAM format. As described herein, an “unmapped” BAM file refersto a BAM file that does not contain aligned sequence read informationand mapping quality parameters and a “mapped” BAM file refers to a BAMfile that contains aligned sequence read information and mapping qualityparameters. As described herein, a “consensus” BAM file refers to a BAMfile that contains consensus compressed data.

In some embodiments, a read structure for a sequencing read withmolecular tagging may include, starting from the 5′ end, a library key,a barcode sequence, a barcode adapter, a prefix molecular tag, asequence template, a suffix molecular tag, and a P1 adapter. Basecalling may include trimming the library key, barcode sequence andbarcode adapter from the rest of the sequencing read and storing them inthe key sequence (KS) tag field of the read group header @RG of the BAMfile format. Base calling may include trimming the P1 adapter from thesequencing read and storing it in a comment line @CO of the BAM header.

In some embodiments, the base caller 202 may be configured to detect thetag structure and trim the tag from the sequencing read. Trimmed tagsmay be stored in the BAM read group header (@RG) in fields for customtags ZT (for a prefix tag, for example) and YT (for a suffix tag, forexample). Since the read group header is associated with the sequencingread data of the template, the integrity of the tag's association withthe family group may be maintained. Subsequent mapping or alignment witha reference sequence may be applied to the template sequence without aprefix tag or a suffix tag. This reduces the possibility of erroneousmapping of a portion of a tag to the reference sequence.

In some embodiments, a tag sequence may include a subset of random basesand a subset of known bases. A tag trimming method may require that thesequence of bases in the tag portion of the sequencing read match theknown bases. A tag trimming method may select a base string that has anumber of bases equal to the known length of a tag. In some embodiments,a tag trimming method may detect and correct sequencing error in thetag, such as insertions and deletions. Correcting sequencing errors inthe tag may provide more accurate family identification.

In some embodiments, the mapped BAM file may store a plurality ofsequence reads, a plurality of vectors of flow space signal measurementsand a plurality of sequence alignments corresponding to the sequencereads. The mapped BAM file may store the vectors of flow space signalmeasurements in the custom tag field ZM. The mapped BAM file may storethe model parameters in the custom tag field ZP. The mapped BAM file maystore the molecular tag sequences associated with the sequence reads inthe BAM read group header, as described above. The mapped BAM file maybe stored in memory and provided to the consensus pipeline 206. In someembodiments, other file formats may be used to store a plurality ofsequence reads, a plurality of vectors of flow space signalmeasurements, a plurality of sequence alignments and molecular tagsequences corresponding to the sequence reads.

FIG. 3 is a block diagram of an exemplary method for the consensuspipeline 206, in accordance with an embodiment. Grouping operations 302may use the molecular tag sequence information to identify families ofsequence reads and corresponding flow space signal measurements.Grouping operations 302 may compare molecular tag sequences associatedwith the sequence reads and apply a grouping threshold. For example, thecriterion of the grouping threshold can require that all tag sequencesof members of a group of sequence reads have 100% tag sequence identity.Sequence reads and corresponding flow space signal measurements that aredetermined to share a common tag sequence, by meeting the criterion ofthe grouping threshold, are grouped into a given family, where thecommon tag sequence is unique to that family. Each family will have anumber of members which is the number of sequence reads grouped in thefamily. In some embodiments, families not having at least a minimumnumber of members will not be further processed and may be removed frommemory. Methods for grouping sequence reads based on molecular tagsequences for use with the present teachings may include one or morefeatures described in U.S. Pat. Appl. Publ. No. 2016/0362748, publishedDec. 15, 2016, incorporated by reference herein in its entirety.

In some embodiments, the flow space consensus compressor 304 maydetermine consensus compressed data based on the flow space signalmeasurements for each of the grouped families as follows:

-   -   a. Calculate an arithmetic mean of the vectors of flow space        signal measurements of each grouped family to form a vector of        consensus flow space signal measurements for each family.    -   b. Calculate a standard deviation of the vectors of flow space        signal measurements of each family to form a vector of standard        deviations for each family.        In some embodiments, the flow space consensus compressor 304 may        receive at least one model parameter corresponding to each        vector of flow space signal measurements. The flow space        consensus compressor 304 may calculate an arithmetic mean of        model parameters of the family to form at least one consensus        model parameter for the family. The model parameters may be used        for base calling, as described below. In some embodiments, the        model parameters may include an incomplete extension (IE)        parameter and a carry forward (CF) parameter for each vector of        flow space signal measurements. The flow space consensus        compressor 304 may calculate an arithmetic mean of the IE        parameters and an arithmetic mean of the CF parameters of each        family to form a consensus IE parameter and a consensus CF        parameter for each family.

In some embodiments, the base caller 202 may be applied to the vector ofconsensus flow space signal measurements for each family to generate aconsensus base sequence for the respective family. The consensus modelparameters may be used in applying a model for base calling. Forexample, a consensus incomplete extension (IE) parameter and a consensuscarry forward (CF) parameter for each family may be provided to the basecaller 202. The base calling may include one or more features describedin U.S. Pat. Appl. Publ. No. 2013/0090860, published Apr. 11, 2013,and/or U.S. Pat. Appl. Publ. No. 2012/0109598, published May 3, 2012,which are all incorporated by reference herein in their entirety. Aconsensus sequence alignment for the consensus base sequence may bedetermined by comparing the consensus base sequence to the sequence readin the family having the highest mapping quality. If the consensus basesequence matches the sequence read having the highest mapping quality,the corresponding sequence alignment is selected as the consensussequence alignment. If the consensus base sequence does not match thesequence read in the family having the highest mapping quality, themapper 204 may align the consensus base sequence to a reference sequenceor a reference genome to determine the consensus sequence alignment.Methods for aligning consensus sequence reads may include one or morefeatures described in U.S. Pat. Appl. Publ. No. 2012/0197623, publishedAug. 2, 2012, incorporated by reference herein in its entirety. In someembodiments, about 1% of consensus sequencing reads, on average, mayneed realignment by the mapper 204.

In some embodiments, the processor may store the consensus compresseddata for each family in a compressed data structure in a memory. Theconsensus compressed data may include the consensus base sequence, theconsensus sequence alignment, the vector of consensus flow space signalmeasurements, the vector of standard deviations and the number ofmembers for each family. The consensus compressed data may furtherinclude a set of consensus model parameters for each family. If thefamily has been separated into flow-synchronized subfamilies, theconsensus compressed data may further include the consensus basesequence, the consensus sequence alignment, the vector of consensus flowspace signal measurements, the vector of standard deviations and thenumber of members for each subfamily. In some embodiments, thecompressed data structure may be compatible with the BAM file format toproduce a mapped consensus BAM file. The BAM specification allows theuser to define custom tag fields. For example, custom tag fields may bedefined for the BAM file used to store some of the consensus compresseddata, as shown in Table 1.

TABLE 1 BAM Custom Tag Field DATA ZM Consensus flow space signalmeasurements ZP Consensus model parameters ZS Standard deviations offlow space signal measurements ZR Number of sequence reads, or members,in family or subfamily

The original sequence reads, original vectors of flow space signalmeasurements and original model parameters for each family are notincluded in the consensus compressed data and may be removed frommemory. In some embodiments, the compressed data structure may use adifferent format protocol than the BAM file format, including customfile formats.

In some embodiments, the compressed data structure, for example themapped consensus BAM file may be provided to the variant caller 208 ofFIG. 2. The variant caller uses the consensus compressed data, ratherthan the original sequence reads and flow space signal measurements forvariant calling, as described below. Methods for variant calling for usewith the present teachings may include one or more features described inU.S. Pat. Appl. Publ. No. 2014/0296080, Oct. 2, 2014, incorporated byreference herein in its entirety.

In some embodiments, the base caller 202 applies a model to determine asequence read from a vector of flow space signal measurements. One suchmodel is represented by the equation:y=Hx+w  (1)where, y represents a vector of flow space signal measurements, xrepresents an ideal flowgram vector, w represents a noise vector, and His a matrix that represents the model transfer function. The idealflowgram vector x represents a vector of integer signal values for agiven base sequence and a given flow order. The integer signal valuescorrespond to the number of nucleotide incorporations at a particularflow in the flow order. For example, for 1-mer, the ideal flowgram valueis 1, for 2-mer, the ideal flowgram value is 2, and for n-mers, theideal flowgram value is n, etc. For example, given a repeating floworder ACGT and a given base sequence GTCGGA, ideal flowgram vector is[0, 0, 1, 1, 0, 1, 2, 0, 1]. Referring to Table 2, the ideal flowgramvector (column 3) may be constructed from the given base sequence(column 1) and flow order (column 2).

TABLE 2 IDEAL HARDCLIPPED BASE FLOW FLOWGRAM FLOWGRAM SEQUENCE ORDERVALUE VALUE A 0 0 C 0 0 G G 1 1 T T 1 1 A 0 0 C C 1 1 GG G 2 1 T 0 0 A A1 1

The model transfer function represented by the matrix H may be based onan underlying signal model or physical model. The model transferfunction may depend on model parameters. In an exemplary model, themodel parameters may include phasing parameters, such as an incompleteextension (IE) parameter and a carry forward (CF) parameter. DeterminingIE and CF parameters are described for use with the present teachingsmay include one or more features described in U.S. Pat. Appl. Publ. No.2012/0197623, published Aug. 2, 2012, incorporated by reference hereinin its entirety.

Ideally, during nucleotide incorporation, each extension reactionassociated with the population of template polynucleotide strands isperforming the same incorporation step at the same sequence position ineach flow cycle, which can generally be referred to as being “in phase”or in “phasic synchrony” with each other. However, it has been observedthat some fraction of template strands in each population may lose orfall out of phasic synchronism with the majority of the template strandsin the population. For a family of sequence reads having a common tagsequence, flow-synchronized sequence reads result from the correspondingpolynucleotide strands generated from the same polynucleotide moleculeincorporating the nucleotide incorporation in phase. Ideally, sequencereads in the same family will have the same flowgram vector. However, itis possible that one or more sequence reads in a family will have adifferent flowgram vector from the other sequence reads in the family.In this situation, the family may be divided into subfamilies, whereeach subfamily has matching flow synchronization. Detecting flowsynchronization of sequence reads in a family is described furtherbelow.

An assumption for the exemplary model described with respect to equation(1) is that the flow-synchronized reads within a family will have thesame ideal flowgram vector x. It is also assumed that the noise vector wis a vector of independent identically distributed Gaussian randomvariables. The arithmetic mean y of the vector y of flow space signalmeasurements, is modeled as follows,

$\begin{matrix}{\overset{\_}{y}\overset{\Delta}{=}{{{\frac{1}{N}{\sum\limits_{n = 1}^{N}\;{y_{n}\frac{1}{N}{\sum\limits_{n = 1}^{N}{{H\left( {{IE}_{n},{CF}_{n}} \right)}ϰ}}}}} + w_{n}} = {{{\overset{\_}{H}ϰ} + \overset{\_}{w}} \approx {{{H\left( {\overset{\_}{IE},\overset{\_}{CF}} \right)}ϰ} + \overset{\_}{w}}}}} & (2)\end{matrix}$where N is the number of sequence reads in the family, n indicatesparameters and vectors corresponding to the n^(th) family member, theoverbars indicate arithmetic mean values of the parameters, vectorelements and matrix elements. The sequence reads in the family areassumed to be flow-synchronized. The ideal flowgram vector x is assumedto be identical for the flow-synchronized sequence reads in the family,so its mean value is the same. The model transfer function H isapproximated by a polynomial expansion as follows,

$\begin{matrix}\begin{matrix}{\left\lbrack \overset{¨}{H} \right\rbrack_{i,j}\overset{\Delta}{=}{\frac{1}{N}{\sum\limits_{n = 1}^{N}\left\lbrack {H\left( {{IE}_{n},{CF}_{n}} \right)} \right\rbrack_{i,j}}}} \\{= {{\frac{1}{N}{\sum\limits_{n = 1}^{N}c_{0}}} + {a_{1}{IE}_{n}} + {b_{1}{CF}_{n}} + {H.O.T.\left( {{IE}_{n},{CF}_{n}} \right)}}} \\{\approx {c_{0} + {a_{1}\overset{\_}{IE}} + {b_{1}\overset{\_}{CF}}}} \\{\approx \left\lbrack {H\left( {\overset{\_}{IE},\overset{\_}{CF}} \right)} \right\rbrack_{i,j}}\end{matrix} & (3)\end{matrix}$where i,j indicate an element of the matrix H and c₀, a₁, and b₁ arecoefficients of the expansion. For the exemplary model, the modelparameters IE and CF have typical values of less than 1%, so the higherorder terms (H.O.T) of the polynomial expansion are insignificant. Theabove equations (1)-(3) show that the arithmetic mean of the vector offlow space signal measurements and the arithmetic mean of the modelparameters corresponding to the sequence reads of the family preservethe useful information for variant calling and base calling.Furthermore, the arithmetic mean of the noise vector w has a reducednoise variance. The mean of the noise may be estimated as a bias by thevariant caller 208, thereby mitigating its effect.

In some embodiments, flow synchronization of sequence reads in a familymay be determined by analyzing the corresponding flowgram vectors asfollows:

-   -   a. Generate a flowgram vector for each sequence read in the        family, the flowgram vector having integer-valued elements. (See        Table 2, column 3)    -   b. Hard clip the integer-valued elements of the flowgram vector        to binary-valued elements, where an element of the hard-clipped        flowgram vector x_(b)(i) is set to 1 when flowgram vector        element x(i)≥1 and x_(b)(i) is set to 0 when x(i)=0. Any values        greater than 1 are associated with homopolymer (HP) lengths, so        the hard-clipped flowgram vector is also referred to as a        HP-compressed flowgram vector. (See Table 2, column 4)    -   c. Compare the hard-clipped flowgram vectors within the family.    -   d. If the hard-clipped flowgram vectors within the family match,        the family is flow-synchronized.    -   e. If the hard-clipped flowgram vectors within the family do not        match, divide the family members into subfamilies where each        subfamily has matching hard-clipped flowgram vectors.

FIG. 5 illustrates an exemplary plot of flow space signal measurementsfor a single family. The flow index indicates the j-th flow in the flowsequence. The normalized amplitude indicates the value of the flow spacesignal measurement. The type of plot symbol corresponds to thenucleotide at the particular flow. This plot of flow space signalmeasurements corresponds to a single flow-synchronized family ofsequence reads associated with a common molecular tag. The values of theflow space signal measurements at each flow are clustered near similarvalues. The flow index corresponds to the element index in the vector offlow space signal measurements. The flow space signal measurementsrepresented in this plot may be input to the flow space consensuscompressor 304.

FIG. 6 illustrates an exemplary plot of consensus flow space signalmeasurements for a single family. This plot shows consensus flow spacesignal measurement values resulting from consensus calculations on theflow space signal measurements shown in FIG. 5. The plot symbolsindicate the arithmetic means that are elements of the vector ofconsensus flow space measurements for the family. The bars indicate thestandard deviations that are elements of the vector of standarddeviations for the family.

FIG. 7 is a block diagram of an exemplary method of using consensuscompressed data for variant calling operations. In some embodiments, thevariant caller 208 may hypothesize a candidate allele and determine alog-likelihood of a predicted flow space signal value for the candidateallele. The log-likelihood of the predicted flow space signal valuecorresponding to a given element of the vector of consensus flow spacesignal measurements is referred to herein as the family log-likelihood.The family log-likelihood may be modeled as a sum of log-likelihoods oforiginal flow space signal measurements y_(n) where the log-likelihoodis based on a non-standardized Student's t distribution, given by thefollowing expression:

$\begin{matrix}\begin{matrix}{{\sum\limits_{n = 1}^{N}{\log\;{L\left( {p❘y_{n}} \right)}}} = {{\sum\limits_{n = 1}^{N}\alpha} - {\beta\;\log\;\left( {1 + \frac{\left( {y_{n} - p} \right)^{2}}{\gamma}} \right)}}} \\{\approx {N\left( {\alpha - {\beta\;\log\;\left( {1 + \frac{\left( {\overset{\_}{y} - p} \right)^{2} + \sigma_{y}^{2}}{\gamma}} \right)}} \right.}}\end{matrix} & (4)\end{matrix}$where p is the predicted flow space signal value at a given position forthe candidate, or hypothesized, allele, y_(n) is an element at the givenposition of the n^(th) vector of original flow space signal measurementsfor the n^(th) member of the family, y is the an element at the givenposition of the vector of consensus flow space signal measurements forthe family, σ_(y) is an element at the given position of the vector ofstandard deviations for the family and N is the number of members in thefamily. The parameters α, β and γ are based on the degrees of freedomparameter and the scale parameter of the non-standardized Student's tdistribution, where α is a function of the degrees of the degrees offreedom and the scale parameter, β is a function of the degrees offreedom parameter and γ is a function of the degrees of freedom and thescale parameter. The degrees of freedom may be set to a particularvalue. The scale parameter may be estimated using any suitable methodfor fitting data to a distribution. In some embodiments, thelog-likelihood may be based on other distributions, such as a Gaussiandistribution.

In some embodiments, the variant caller 208 may update the prediction bymodifying the predicted flow space signal value p so that (y−p) has amean value of 0. In some embodiments, log-likelihoods of two candidate,or hypothesized, alleles u and v, are estimated for a decision on whichcandidate allele is more likely to be present. The variant caller 208may estimate a bias, where difference d_(j)=p_(j) ^(u)−p_(j) ^(v), isthe difference in predicted flow space signal values predicted under thetwo candidate, or hypothesized, alleles u and v, as follows:

$\begin{matrix}{\beta_{f} = {\left( {\sum\frac{d_{ij}^{2}}{\sigma_{ij}^{2}}} \right)^{- 1}*\left( {\sum\frac{N_{i}d_{ij}^{2}*\left( {{\rho_{i}*\left( {m_{ij} - p_{ij}^{u}} \right)} + {\left( {1 - \rho_{i}} \right)*\left( {m_{ij} - p_{ij}^{v}} \right)}} \right)}{\sigma_{ij}^{2}}} \right)}} & (5)\end{matrix}$where β_(f), is the bias for a forward sequence read, σ_(ij) ², is anestimate of the variance for the j-th flow in the i-th consensus readcorresponding to the i-th family, N_(i) is the number of members in thei-th family, m_(ij) is the consensus flow space signal measurementcorresponding to the i-th consensus read and the j-th flow index, andρ_(i) is the family responsibility for the i-th family. The summationsare taken over all consensus reads on the forward strand and allrelevant flows. Note that the consensus flow space signal measurement yin equation (4) corresponds to m_(ij) where j indicates the j-th elementof the vector of consensus flow space signal measurements, σ_(y) inequation (4) corresponds to σ_(ij) and i indicates the i-th family. Forthe reverse bias β_(r), the bias on the reads mapping to the reversestrand may be estimated similarly, except that the sum is then takenover all consensus reads on the reverse strand and all relevant flows.The family responsibility may be estimated by the following expression:

$\begin{matrix}{\rho_{i} = \frac{f}{f + {\left( {1 - f} \right)^{*}{\exp\left( {{LL_{vi}} - {LL}_{ui}} \right)}}}} & (6)\end{matrix}$where f represents the allele frequency of candidate, or hypothesized,allele u and (1−f) represent the allele frequency of candidate, orhypothesized, allele v, and LL_(ui) and LL_(vi) are log-likelihoodsestimated using equation (4), where p, the predicted flow space signalmeasurement would correspond to candidate allele u in LL_(ui) or tocandidate allele v in LL_(vi). The consensus read responsibility can beupdated from the family responsibility as follows:

$\begin{matrix}{\rho_{ni} = \frac{\rho_{i}}{\rho_{i} + {\left( {1 - \rho_{i}} \right)*{\exp\left( {{LL_{vn}} - {LL_{un}}} \right)}}}} & (7)\end{matrix}$where ρ_(ni) is the consensus read responsibility for the n-th member ofthe i-th family and LL_(un) and LL_(vn) are log-likelihoods of the n-thconsensus flow space signal measurement for candidate alleles u and v,respectively. In some embodiments, iterations of the estimations andupdates for the steps shown in FIG. 7 may be repeated until convergenceor repeated for a fixed number of iterations.

In some embodiments, variant calling may use hypothesis testing. For anexample of two hypotheses, suppose a candidate, or hypothesized, alleleu represents to a reference sequence and a candidate, or hypothesized,allele v, represents a variant sequence. Hypotheses to be tested for thepresence of the variant can be expressed by:H _(REF)={Allele Frequency f≤Minimum Allele Frequency f _(c)}H _(VAR)={Allele Frequency f>Minimum Allele Frequency f _(c)}The minimum allele frequency f_(c) may be a parameter set by a user.Applying a maximum a posteriori probability criterion gives thefollowing decisions:APP(H _(REF))≥APP(H _(VAR)):H _(REF) is more probableAPP(H _(REF))<APP(H _(VAR)):H _(VAR) is more probablewhere APP indicates the a posteriori probability given the ensemble ofread sequences. A quality value, QUAL, for the call is given by,QUAL=min{APP(H _(REF)),APP(H _(VAR))}  (8)The a posteriori probabilities are given by the expressions:APP(H _(REF))=∫₀ ^(f) ^(c) f _(APP)(AF=f)df  (9)APP(H _(VAR))=∫_(f) _(c) ¹ f _(APP)(AF=f)df  (10)where f_(APP) is a posteriori probability density function for allelefrequencies AF=f, and f_(c) is the minimum allele frequency for decidinga variant is present at the location. FIG. 8 illustrates an example of aposteriori probabilities APP(H_(REF)) and APP(H_(VAR)) found by theintegrations of equations (9) and (10).

FIGS. 9A, 9B and 9C show exemplary comparisons of results of variantcalling based on consensus compressed data from a mapped consensus BAMfile and original data from an original mapped BAM file. The mappedconsensus BAM file was generated by the consensus pipeline 206 from theoriginal mapped BAM file. Points along the diagonal indicate that thesame results for both the consensus compressed data and the originaldata. FIG. 9A shows a plot of an exemplary comparison of quality values,QUAL, of variants called using the original data from the mapped BAMfile versus consensus compressed data from the mapped consensus BAMfile. FIG. 9B shows a plot of an exemplary comparison of radius of bias(RBI), estimated using the original data from the mapped BAM file versusconsensus compressed data from the mapped consensus BAM file. The RBI iscalculated as follows:RBI=SQRT(β_(f) ²+β_(r) ²)  (11)where β_(f) is the forward bias and β_(r) is the reverse bias, asdescribed above. When there are only forward sequence reads, the β_(r)term is omitted in equation (11). FIG. 9C shows a plot of an exemplarycomparison of allele frequencies (AF), of variants called using theoriginal data from the BAM file versus consensus compressed data fromthe consensus BAM file. FIGS. 9A, 9B and 9C show that variant callingresults based on consensus compressed data are very similar to variantcalling result using the original data. Therefore, using the consensuscompressed data has not compromised the quality of the variant callingresults.

Table 3 summarizes results of flow space consensus compression for threeexamples.

TABLE 3 ITEM EXAMPLE 1 EXAMPLE 2 EXAMPLE 3 Number of functional familiesidentified 102,301 270,214 1,058,962 Number of sequence reads in thefunctional 11,375,156 2,380,319 11,528,485 families identified Number ofconsensus sequence reads generated 122,940 306,367 2,198,685 Number ofconsensus sequence reads realigned by 13,903 113,668 84,894 mapper 204Average number of consensus sequence reads per 1.20175 1.13379 2.07626functional family Compression rate 0.0108078 0.128708 0.190718

The number of functional families identified is a count of the number offamilies identified by the grouping operations 302 that have a number ofmembers greater than the minimum family size. For example, thefunctional families may have a family size of three or more members. Thenumber of sequence reads in the functional families identified is acount of all the sequence reads that are members of functional families.The number of consensus sequence reads generated is a count of the totalnumber of consensus reads generated for both families and subfamilies.The number of consensus sequence reads realigned by mapper 204 is acount of consensus reads from both families and subfamilies that wererealigned. The average number of consensus sequence reads per functionalfamily is a ratio of the number of consensus sequence reads to thenumber of functional families. The average number of consensus sequencereads per functional family is an indicator of subfamilies generated perfamily, e.g. for flow synchronization. The compression rate iscalculated by dividing the number of consensus sequence reads by thenumber of sequence reads in the functional families. The number ofconsensus sequence reads is proportional to the amount of consensuscompressed data and the number of sequence reads in the functionalfamilies is proportional to the amount of original read data, so theirratio gives a measure of compression rate.

The compression rates show reduction in the amount of data for theconsensus compressed data from the original read data. The compressionrates are related to the reduction in the amount of memory required tostore the consensus compressed data from the amount of memory requiredto store the original read data.

Example 1 represents results for unidirectional sequencing and higheramplification. The compression rate is about 0.01. Example 2 representsresults for unidirectional sequencing and lower amplification. Thecompression rate is about 0.13. The average number of consensus readsper functional family is lower than for Example 1. Example 3 representsresults for bidirectional sequencing (forward and reverse readdirections). For bidirectional sequencing, each family includes at leasttwo subfamilies, one subfamily for forward sequence reads and one familyfor reverse sequence reads. Other subfamilies may be added for flowsynchronization. The compression rate for Example 3 is about 0.19.

The results of Table 3 and of FIGS. 9A-9C show that compression isachieved without compromising variant calling results. The consensuscompressed BAM file has smaller memory requirements than the originalBAM file. Furthermore, the computational requirements for variantcalling using the consensus compressed data are reduced. Thecomputational requirement for variant calling is on the order of M log Moperations, where M is the number of sequence reads processed forvariant calling. For variant calling using the original sequence reads,M would equal the number of sequence reads in the functional families(see Table 3). For variant calling using the consensus sequence reads, Mwould equal the number of consensus sequence reads (see Table 3). Thereduced computations using the consensus sequence reads result in fastervariant calling times.

According to an exemplary embodiment, there is provided a method forcompressing molecular tagged nucleic acid sequence data, comprising (a)receiving a plurality of nucleic acid sequence reads, a plurality ofvectors of flow space signal measurements and a plurality of sequencealignments, wherein each sequence read is associated with a moleculartag sequence, the molecular tag sequence identifying a family ofsequence reads resulting from a particular polynucleotide molecule in anucleic acid sample, wherein each vector of flow space signalmeasurements and each sequence alignment correspond with one of thesequence reads; (b) grouping sequence reads associated with a samemolecular tag sequence to form a family of sequence reads, correspondingvectors of flow space signal measurements and corresponding sequencealignments, each family having a number of members; (c) calculating anarithmetic mean of the corresponding vectors of flow space signalmeasurements to form a vector of consensus flow space signalmeasurements for the family; (d) calculating a standard deviation of thecorresponding vectors of flow space signal measurements to form a vectorof standard deviations for the family; (e) determining a consensus basesequence based on the vector of consensus flow space signal measurementsfor the family; (f) determining a consensus sequence alignment bycomparing the consensus base sequence to the sequence read having ahighest mapping quality of the corresponding sequence alignments for thefamily; and (g) generating a compressed data structure comprisingconsensus compressed data, the consensus compressed data including foreach family, the consensus base sequence, the consensus sequencealignment, the vector of consensus flow space signal measurements, thevector of standard deviations and the number of members. The method mayfurther comprise determining whether the sequence reads of the familyare flow synchronized. The method may further comprise defining asubfamily of the family based on a matching flow synchronization,wherein the sequence reads of the subfamily are flow synchronized. Themethod may further comprise performing the steps of calculating anarithmetic mean of the vectors of flow space signal measurements,calculating a standard deviation of the vectors of flow space signalmeasurements, and determining a consensus base sequence for the sequencereads for the subfamily of the family, wherein the generating acompressed data structure includes consensus compressed data for thesubfamily of the family. The step of receiving may further includereceiving at least one model parameter corresponding to each vector offlow space signal measurements, wherein the method further comprisescalculating an arithmetic mean of the model parameters of thecorresponding vectors of flow space signal measurements of the family toform at least one consensus model parameter for the family, wherein thegenerating the compressed data structure includes the consensus modelparameters in the consensus compressed data. The step of determining aconsensus base sequence for the sequence reads of the family may befurther based on the at least one consensus model parameter for thefamily. The at least one model parameter may comprise an incompleteextension (IE) parameter. The at least one model parameter may comprisea carry forward (CF) parameter. The method may further comprisedetermining a variant in a given consensus base sequence using at leasta portion of the consensus compressed data from the compressed datastructure. The step of determining a variant may be based on the vectorof consensus flow space signal measurements and the vector of standarddeviations corresponding to the given consensus base sequence. The stepof determining a variant may further comprise estimating alog-likelihood of a predicted flow space signal value for a candidateallele based on a function of the consensus flow space signalmeasurement at a given position in the vector of consensus flow spacesignal measurements and the standard deviation at the given position inthe vector of standard deviations. The compressed data structure may becompatible with a BAM file format. The method may further comprisemapping the consensus base sequence to a reference genome to generatethe consensus sequence alignment when the consensus base sequence doesnot match the sequence read in the family having the highest mappingquality. In such a method, the plurality of nucleic acid sequence readsmay comprise forward sequence reads and reverse sequence reads, whereinthe step of grouping sequence reads may further comprise identifying twosubfamilies for the family, wherein a first subfamily contains theforward sequence reads and a second subfamily contains the reversesequence reads. The method may further comprise performing the steps ofcalculating an arithmetic mean of the vectors of flow space signalmeasurements, calculating a standard deviation of the vectors of flowspace signal measurements, and determining a consensus base sequence forthe sequence reads for each of the two subfamilies of the family,wherein the generating a compressed data structure includes consensuscompressed data for the two subfamilies of the family.

According to an exemplary embodiment, there is provided a non-transitorymachine-readable storage medium comprising instructions which, whenexecuted by a processor, cause the processor to perform a method forcompressing molecular tagged nucleic acid sequence data, comprising:comprising (a) receiving a plurality of nucleic acid sequence reads, aplurality of vectors of flow space signal measurements and a pluralityof sequence alignments, wherein each sequence read is associated with amolecular tag sequence, the molecular tag sequence identifying a familyof sequence reads resulting from a particular polynucleotide molecule ina nucleic acid sample, wherein each vector of flow space signalmeasurements and each sequence alignment correspond with one of thesequence reads; (b) grouping sequence reads associated with a samemolecular tag sequence to form a family of sequence reads, correspondingvectors of flow space signal measurements and corresponding sequencealignments, each family having a number of members; (c) calculating anarithmetic mean of the corresponding vectors of flow space signalmeasurements to form a vector of consensus flow space signalmeasurements for the family; (d) calculating a standard deviation of thecorresponding vectors of flow space signal measurements to form a vectorof standard deviations for the family; (e) determining a consensus basesequence based on the vector of consensus flow space signal measurementsfor the family; (f) determining a consensus sequence alignment bycomparing the consensus base sequence to the sequence read having ahighest mapping quality of the corresponding sequence alignments for thefamily; and (g) generating a compressed data structure comprisingconsensus compressed data, the consensus compressed data including foreach family, the consensus base sequence, the consensus sequencealignment, the vector of consensus flow space signal measurements, thevector of standard deviations and the number of members. The method mayfurther comprise determining whether the sequence reads of the familyare flow synchronized. The method may further comprise defining asubfamily of the family based on a matching flow synchronization,wherein the sequence reads of the subfamily are flow synchronized. Themethod may further comprise performing the steps of calculating anarithmetic mean of the vectors of flow space signal measurements,calculating a standard deviation of the vectors of flow space signalmeasurements, and determining a consensus base sequence for the sequencereads for the subfamily of the family, wherein the generating acompressed data structure includes consensus compressed data for thesubfamily of the family. The step of receiving may further includereceiving at least one model parameter corresponding to each vector offlow space signal measurements, wherein the method further comprisescalculating an arithmetic mean of the model parameters of thecorresponding vectors of flow space signal measurements of the family toform at least one consensus model parameter for the family, wherein thegenerating the compressed data structure includes the consensus modelparameters in the consensus compressed data. The step of determining aconsensus base sequence for the sequence reads of the family may befurther based on the at least one consensus model parameter for thefamily. The at least one model parameter may comprise an incompleteextension (IE) parameter. The at least one model parameter may comprisea carry forward (CF) parameter. The method may further comprisedetermining a variant in a given consensus base sequence using at leasta portion of the consensus compressed data from the compressed datastructure. The step of determining a variant may be based on the vectorof consensus flow space signal measurements and the vector of standarddeviations corresponding to the given consensus base sequence. The stepof determining a variant may further comprise estimating alog-likelihood of a predicted flow space signal value for a candidateallele based on a function of the consensus flow space signalmeasurement at a given position in the vector of consensus flow spacesignal measurements and the standard deviation at the given position inthe vector of standard deviations. The compressed data structure may becompatible with a BAM file format. The method may further comprisemapping the consensus base sequence to a reference genome to generatethe consensus sequence alignment when the consensus base sequence doesnot match the sequence read in the family having the highest mappingquality. In such a method, the plurality of nucleic acid sequence readsmay comprise forward sequence reads and reverse sequence reads, whereinthe step of grouping sequence reads may further comprise identifying twosubfamilies for the family, wherein a first subfamily contains theforward sequence reads and a second subfamily contains the reversesequence reads. The method may further comprise performing the steps ofcalculating an arithmetic mean of the vectors of flow space signalmeasurements, calculating a standard deviation of the vectors of flowspace signal measurements, and determining a consensus base sequence forthe sequence reads for each of the two subfamilies of the family,wherein the generating a compressed data structure includes consensuscompressed data for the two subfamilies of the family.

According to an exemplary embodiment, there is provided a system forcompressing molecular tagged nucleic acid sequence data, including: amachine-readable memory and a processor configured to executemachine-readable instructions, which, when executed by the processor,cause the system to perform a method for compressing molecular taggednucleic acid sequence data, comprising: comprising (a) receiving aplurality of nucleic acid sequence reads, a plurality of vectors of flowspace signal measurements and a plurality of sequence alignments,wherein each sequence read is associated with a molecular tag sequence,the molecular tag sequence identifying a family of sequence readsresulting from a particular polynucleotide molecule in a nucleic acidsample, wherein each vector of flow space signal measurements and eachsequence alignment correspond with one of the sequence reads; (b)grouping sequence reads associated with a same molecular tag sequence toform a family of sequence reads, corresponding vectors of flow spacesignal measurements and corresponding sequence alignments, each familyhaving a number of members; (c) calculating an arithmetic mean of thecorresponding vectors of flow space signal measurements to form a vectorof consensus flow space signal measurements for the family; (d)calculating a standard deviation of the corresponding vectors of flowspace signal measurements to form a vector of standard deviations forthe family; (e) determining a consensus base sequence based on thevector of consensus flow space signal measurements for the family; (f)determining a consensus sequence alignment by comparing the consensusbase sequence to the sequence read having a highest mapping quality ofthe corresponding sequence alignments for the family; and (g) generatinga compressed data structure comprising consensus compressed data, theconsensus compressed data including for each family, the consensus basesequence, the consensus sequence alignment, the vector of consensus flowspace signal measurements, the vector of standard deviations and thenumber of members. The method may further comprise determining whetherthe sequence reads of the family are flow synchronized. The method mayfurther comprise defining a subfamily of the family based on a matchingflow synchronization, wherein the sequence reads of the subfamily areflow synchronized. The method may further comprise performing the stepsof calculating an arithmetic mean of the vectors of flow space signalmeasurements, calculating a standard deviation of the vectors of flowspace signal measurements, and determining a consensus base sequence forthe sequence reads for the subfamily of the family, wherein thegenerating a compressed data structure includes consensus compresseddata for the subfamily of the family. The step of receiving may furtherinclude receiving at least one model parameter corresponding to eachvector of flow space signal measurements, wherein the method furthercomprises calculating an arithmetic mean of the model parameters of thecorresponding vectors of flow space signal measurements of the family toform at least one consensus model parameter for the family, wherein thegenerating the compressed data structure includes the consensus modelparameters in the consensus compressed data. The step of determining aconsensus base sequence for the sequence reads of the family may befurther based on the at least one consensus model parameter for thefamily. The at least one model parameter may comprise an incompleteextension (IE) parameter. The at least one model parameter may comprisea carry forward (CF) parameter. The method may further comprisedetermining a variant in a given consensus base sequence using at leasta portion of the consensus compressed data from the compressed datastructure. The step of determining a variant may be based on the vectorof consensus flow space signal measurements and the vector of standarddeviations corresponding to the given consensus base sequence. The stepof determining a variant may further comprise estimating alog-likelihood of a predicted flow space signal value for a candidateallele based on a function of the consensus flow space signalmeasurement at a given position in the vector of consensus flow spacesignal measurements and the standard deviation at the given position inthe vector of standard deviations. The compressed data structure may becompatible with a BAM file format. The method may further comprisemapping the consensus base sequence to a reference genome to generatethe consensus sequence alignment when the consensus base sequence doesnot match the sequence read in the family having the highest mappingquality. In such a method, the plurality of nucleic acid sequence readsmay comprise forward sequence reads and reverse sequence reads, whereinthe step of grouping sequence reads may further comprise identifying twosubfamilies for the family, wherein a first subfamily contains theforward sequence reads and a second subfamily contains the reversesequence reads. The method may further comprise performing the steps ofcalculating an arithmetic mean of the vectors of flow space signalmeasurements, calculating a standard deviation of the vectors of flowspace signal measurements, and determining a consensus base sequence forthe sequence reads for each of the two subfamilies of the family,wherein the generating a compressed data structure includes consensuscompressed data for the two subfamilies of the family.

Nucleic acid sequence data can be generated using various techniques,platforms or technologies, including, but not limited to: capillaryelectrophoresis, microarrays, ligation-based systems, polymerase-basedsystems, hybridization-based systems, direct or indirect nucleotideidentification systems, pyrosequencing, ion- or pH-based detectionsystems, electronic signature-based systems, etc.

Various embodiments of nucleic acid sequencing platforms, such as anucleic acid sequencer, can include components as displayed in the blockdiagram of FIG. 10. According to various embodiments, sequencinginstrument 1200 can include a fluidic delivery and control unit 1202, asample processing unit 1204, a signal detection unit 1206, and a dataacquisition, analysis and control unit 1208. Various embodiments ofinstrumentation, reagents, libraries and methods used for nextgeneration sequencing are described in U.S. Patent ApplicationPublication No. 2009/0127589 and No. 2009/0026082. Various embodimentsof instrument 1200 can provide for automated sequencing that can be usedto gather sequence information from a plurality of sequences inparallel, such as substantially simultaneously.

In various embodiments, the fluidics delivery and control unit 1202 caninclude reagent delivery system. The reagent delivery system can includea reagent reservoir for the storage of various reagents. The reagentscan include RNA-based primers, forward/reverse DNA primers,oligonucleotide mixtures for ligation sequencing, nucleotide mixturesfor sequencing-by-synthesis, optional ECC oligonucleotide mixtures,buffers, wash reagents, blocking reagent, stripping reagents, and thelike. Additionally, the reagent delivery system can include a pipettingsystem or a continuous flow system which connects the sample processingunit with the reagent reservoir.

In various embodiments, the sample processing unit 1204 can include asample chamber, such as flow cell, a substrate, a micro-array, amulti-well tray, or the like. The sample processing unit 1204 caninclude multiple lanes, multiple channels, multiple wells, or othermeans of processing multiple sample sets substantially simultaneously.Additionally, the sample processing unit can include multiple samplechambers to enable processing of multiple runs simultaneously. Inparticular embodiments, the system can perform signal detection on onesample chamber while substantially simultaneously processing anothersample chamber. Additionally, the sample processing unit can include anautomation system for moving or manipulating the sample chamber.

In various embodiments, the signal detection unit 1206 can include animaging or detection sensor. For example, the imaging or detectionsensor can include a CCD, a CMOS, an ion or chemical sensor, such as anion sensitive layer overlying a CMOS or FET, a current or voltagedetector, or the like. The signal detection unit 1206 can include anexcitation system to cause a probe, such as a fluorescent dye, to emit asignal. The excitation system can include an illumination source, suchas arc lamp, a laser, a light emitting diode (LED), or the like. Inparticular embodiments, the signal detection unit 1206 can includeoptics for the transmission of light from an illumination source to thesample or from the sample to the imaging or detection sensor.Alternatively, the signal detection unit 1206 may provide for electronicor non-photon based methods for detection and consequently not includean illumination source. In various embodiments, electronic-based signaldetection may occur when a detectable signal or species is producedduring a sequencing reaction. For example, a signal can be produced bythe interaction of a released byproduct or moiety, such as a releasedion, such as a hydrogen ion, interacting with an ion or chemicalsensitive layer. In other embodiments a detectable signal may arise as aresult of an enzymatic cascade such as used in pyrosequencing (see, forexample, U.S. Patent Application Publication No. 2009/0325145) wherepyrophosphate is generated through base incorporation by a polymerasewhich further reacts with ATP sulfurylase to generate ATP in thepresence of adenosine 5′ phosphosulfate wherein the ATP generated may beconsumed in a luciferase mediated reaction to generate achemiluminescent signal. In another example, changes in an electricalcurrent can be detected as a nucleic acid passes through a nanoporewithout the need for an illumination source.

In various embodiments, a data acquisition analysis and control unit1208 can monitor various system parameters. The system parameters caninclude temperature of various portions of instrument 1200, such assample processing unit or reagent reservoirs, volumes of variousreagents, the status of various system subcomponents, such as amanipulator, a stepper motor, a pump, or the like, or any combinationthereof.

It will be appreciated by one skilled in the art that variousembodiments of instrument 1200 can be used to practice variety ofsequencing methods including ligation-based methods, sequencing bysynthesis, single molecule methods, nanopore sequencing, and othersequencing techniques.

In various embodiments, the sequencing instrument 1200 can determine thesequence of a nucleic acid, such as a polynucleotide or anoligonucleotide. The nucleic acid can include DNA or RNA, and can besingle stranded, such as ssDNA and RNA, or double stranded, such asdsDNA or a RNA/cDNA pair. In various embodiments, the nucleic acid caninclude or be derived from a fragment library, a mate pair library, aChIP fragment, or the like. In particular embodiments, the sequencinginstrument 1200 can obtain the sequence information from a singlenucleic acid molecule or from a group of substantially identical nucleicacid molecules.

In various embodiments, sequencing instrument 1200 can output nucleicacid sequencing read data in a variety of different output data filetypes/formats, including, but not limited to: *.fasta, *.csfasta,*seq.txt, *qseq.txt, *.fastq, *.sff, *prb.txt, *.sms, *srs and/or *.qv.

According to various exemplary embodiments, one or more features of anyone or more of the above-discussed teachings and/or exemplaryembodiments may be performed or implemented using appropriatelyconfigured and/or programmed hardware and/or software elements.Determining whether an embodiment is implemented using hardware and/orsoftware elements may be based on any number of factors, such as desiredcomputational rate, power levels, heat tolerances, processing cyclebudget, input data rates, output data rates, memory resources, data busspeeds, etc., and other design or performance constraints.

Examples of hardware elements may include processors, microprocessors,input(s) and/or output(s) (I/O) device(s) (or peripherals) that arecommunicatively coupled via a local interface circuit, circuit elements(e.g., transistors, resistors, capacitors, inductors, and so forth),integrated circuits, application specific integrated circuits (ASIC),programmable logic devices (PLD), digital signal processors (DSP), fieldprogrammable gate array (FPGA), logic gates, registers, semiconductordevice, chips, microchips, chip sets, and so forth. The local interfacemay include, for example, one or more buses or other wired or wirelessconnections, controllers, buffers (caches), drivers, repeaters andreceivers, etc., to allow appropriate communications between hardwarecomponents. A processor is a hardware device for executing software,particularly software stored in memory. The processor can be any custommade or commercially available processor, a central processing unit(CPU), an auxiliary processor among several processors associated withthe computer, a semiconductor based microprocessor (e.g., in the form ofa microchip or chip set), a macroprocessor, or generally any device forexecuting software instructions. A processor can also represent adistributed processing architecture. The I/O devices can include inputdevices, for example, a keyboard, a mouse, a scanner, a microphone, atouch screen, an interface for various medical devices and/or laboratoryinstruments, a bar code reader, a stylus, a laser reader, aradio-frequency device reader, etc. Furthermore, the I/O devices alsocan include output devices, for example, a printer, a bar code printer,a display, etc. Finally, the I/O devices further can include devicesthat communicate as both inputs and outputs, for example, amodulator/demodulator (modem; for accessing another device, system, ornetwork), a radio frequency (RF) or other transceiver, a telephonicinterface, a bridge, a router, etc.

Examples of software may include software components, programs,applications, computer programs, application programs, system programs,machine programs, operating system software, middleware, firmware,software modules, routines, subroutines, functions, methods, procedures,software interfaces, application program interfaces (API), instructionsets, computing code, computer code, code segments, computer codesegments, words, values, symbols, or any combination thereof. A softwarein memory may include one or more separate programs, which may includeordered listings of executable instructions for implementing logicalfunctions. The software in memory may include a system for identifyingdata streams in accordance with the present teachings and any suitablecustom made or commercially available operating system (O/S), which maycontrol the execution of other computer programs such as the system, andprovides scheduling, input-output control, file and data management,memory management, communication control, etc.

According to various exemplary embodiments, one or more features of anyone or more of the above-discussed teachings and/or exemplaryembodiments may be performed or implemented using appropriatelyconfigured and/or programmed non-transitory machine-readable medium orarticle that may store an instruction or a set of instructions that, ifexecuted by a machine, may cause the machine to perform a method and/oroperations in accordance with the exemplary embodiments. Such a machinemay include, for example, any suitable processing platform, computingplatform, computing device, processing device, computing system,processing system, computer, processor, scientific or laboratoryinstrument, etc., and may be implemented using any suitable combinationof hardware and/or software. The machine-readable medium or article mayinclude, for example, any suitable type of memory unit, memory device,memory article, memory medium, storage device, storage article, storagemedium and/or storage unit, for example, memory, removable ornon-removable media, erasable or non-erasable media, writeable orre-writeable media, digital or analog media, hard disk, floppy disk,read-only memory compact disc (CD-ROM), recordable compact disc (CD-R),rewriteable compact disc (CD-RW), optical disk, magnetic media,magneto-optical media, removable memory cards or disks, various types ofDigital Versatile Disc (DVD), a tape, a cassette, etc., including anymedium suitable for use in a computer. Memory can include any one or acombination of volatile memory elements (e.g., random access memory(RAM, such as DRAM, SRAM, SDRAM, etc.)) and nonvolatile memory elements(e.g., ROM, EPROM, EEROM, Flash memory, hard drive, tape, CDROM, etc.).Moreover, memory can incorporate electronic, magnetic, optical, and/orother types of storage media. Memory can have a distributed architecturewhere various components are situated remote from one another, but arestill accessed by the processor. The instructions may include anysuitable type of code, such as source code, compiled code, interpretedcode, executable code, static code, dynamic code, encrypted code, etc.,implemented using any suitable high-level, low-level, object-oriented,visual, compiled and/or interpreted programming language.

According to various exemplary embodiments, one or more features of anyone or more of the above-discussed teachings and/or exemplaryembodiments may be performed or implemented at least partly using adistributed, clustered, remote, or cloud computing resource.

According to various exemplary embodiments, one or more features of anyone or more of the above-discussed teachings and/or exemplaryembodiments may be performed or implemented using a source program,executable program (object code), script, or any other entity comprisinga set of instructions to be performed. When a source program, theprogram can be translated via a compiler, assembler, interpreter, etc.,which may or may not be included within the memory, so as to operateproperly in connection with the O/S. The instructions may be writtenusing (a) an object oriented programming language, which has classes ofdata and methods, or (b) a procedural programming language, which hasroutines, subroutines, and/or functions, which may include, for example,C, C++, R, Pascal, Basic, Fortran, Cobol, Perl, Java, and Ada.

According to various exemplary embodiments, one or more of theabove-discussed exemplary embodiments may include transmitting,displaying, storing, printing or outputting to a user interface device,a computer readable storage medium, a local computer system or a remotecomputer system, information related to any information, signal, data,and/or intermediate or final results that may have been generated,accessed, or used by such exemplary embodiments. Such transmitted,displayed, stored, printed or outputted information can take the form ofsearchable and/or filterable lists of runs and reports, pictures,tables, charts, graphs, spreadsheets, correlations, sequences, andcombinations thereof, for example.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention. It is intended thatthe following claims define the scope of the invention and that methodsand structures within the scope of these claims and their equivalents becovered thereby.

What is claimed is:
 1. A method for compressing molecular tagged nucleicacid sequence data, comprising: receiving a plurality of nucleic acidsequence reads, a plurality of vectors of flow space signal measurementsand a plurality of sequence alignments, wherein each sequence read isassociated with a molecular tag sequence, the molecular tag sequenceidentifying a family of sequence reads resulting from a particularpolynucleotide molecule in a nucleic acid sample, wherein each vector offlow space signal measurements and each sequence alignment correspondwith one of the sequence reads; grouping sequence reads associated witha same molecular tag sequence to form a family of sequence reads,corresponding vectors of flow space signal measurements andcorresponding sequence alignments, each family having a number ofmembers; calculating an arithmetic mean of the corresponding vectors offlow space signal measurements to form a vector of consensus flow spacesignal measurements for the family, wherein the vectors of flow spacesignal measurements correspond to the same molecular tag sequence;calculating a standard deviation of the corresponding vectors of flowspace signal measurements to form a vector of standard deviations forthe family, wherein the vectors of flow space signal measurementscorrespond to the same molecular tag sequence; determining a consensusbase sequence based on the vector of consensus flow space signalmeasurements for the family; determining a consensus sequence alignmentby comparing the consensus base sequence to the sequence read having ahighest mapping quality of the corresponding sequence alignments for thefamily; and generating a compressed data structure comprising consensuscompressed data, the consensus compressed data including for eachfamily, the consensus base sequence, the consensus sequence alignment,the vector of consensus flow space signal measurements, the vector ofstandard deviations and the number of members.
 2. The method of claim 1,further comprising determining whether the sequence reads of the familyare flow synchronized.
 3. The method of claim 1, further comprisingdefining a subfamily of the family based on a matching flowsynchronization, wherein the sequence reads of the subfamily are flowsynchronized.
 4. The method of claim 3, further comprising performingthe steps of calculating an arithmetic mean of the vectors of flow spacesignal measurements, calculating a standard deviation of the vectors offlow space signal measurements, and determining a consensus basesequence for the sequence reads for the subfamily of the family, whereinthe generating a compressed data structure includes consensus compresseddata for the subfamily of the family.
 5. The method of claim 1, whereinthe receiving further includes receiving at least one model parametercorresponding to each vector of flow space signal measurements, whereinthe method further comprises calculating an arithmetic mean of the modelparameters of the corresponding vectors of flow space signalmeasurements of the family to form at least one consensus modelparameter for the family, wherein the generating the compressed datastructure includes the consensus model parameters in the consensuscompressed data.
 6. The method of claim 5, wherein the determining aconsensus base sequence for the sequence reads of the family is furtherbased on the at least one consensus model parameter for the family. 7.The method of claim 5, wherein the at least one model parametercomprises an incomplete extension (IE) parameter.
 8. The method of claim5, wherein the at least one model parameter comprises a carry forward(CF) parameter.
 9. The method of claim 1, further comprising determininga variant in a given consensus base sequence using at least a portion ofthe consensus compressed data from the compressed data structure. 10.The method of claim 9, wherein the determining a variant is based on thevector of consensus flow space signal measurements and the vector ofstandard deviations corresponding to the given consensus base sequence.11. The method of claim 9, wherein the determining a variant furthercomprises estimating a log-likelihood of a predicted flow space signalvalue for a candidate allele based on a function of the consensus flowspace signal measurement at a given position in the vector of consensusflow space signal measurements and the standard deviation at the givenposition in the vector of standard deviations.
 12. The method of claim1, wherein the compressed data structure is compatible with a BAM fileformat.
 13. The method of claim 1, comprising mapping the consensus basesequence to a reference genome to generate the consensus sequencealignment when the consensus base sequence does not match the sequenceread in the family having the highest mapping quality.
 14. The method ofclaim 1, wherein the plurality of nucleic acid sequence reads comprisesforward sequence reads and reverse sequence reads, wherein the groupingsequence reads further comprises identifying two subfamilies for thefamily, wherein a first subfamily contains the forward sequence readsand a second subfamily contains the reverse sequence reads.
 15. Themethod of claim 14, further comprising performing the steps ofcalculating an arithmetic mean of the vectors of flow space signalmeasurements, calculating a standard deviation of the vectors of flowspace signal measurements, and determining a consensus base sequence forthe sequence reads for each of the two subfamilies of the family,wherein the generating a compressed data structure includes consensuscompressed data for the two subfamilies of the family.
 16. Anon-transitory machine-readable storage medium comprising instructionswhich, when executed by a processor, cause the processor to perform amethod for compressing molecular tagged nucleic acid sequence data,comprising: receiving a plurality of nucleic acid sequence reads, aplurality of vectors of flow space signal measurements and a pluralityof sequence alignments, wherein each sequence read is associated with amolecular tag sequence, the molecular tag sequence identifying a familyof sequence reads resulting from a particular polynucleotide molecule ina nucleic acid sample, wherein each vector of flow space signalmeasurements and each sequence alignment correspond with one of thesequence reads; grouping sequence reads associated with a same moleculartag sequence to form a family of sequence reads, corresponding vectorsof flow space signal measurements and corresponding sequence alignments,each family having a number of members; calculating an arithmetic meanof the corresponding vectors of flow space signal measurements to form avector of consensus flow space signal measurements for the family,wherein the vectors of flow space signal measurements correspond to thesame molecular tag sequence; calculating a standard deviation of thecorresponding vectors of flow space signal measurements to form a vectorof standard deviations for the family, wherein the vectors of flow spacesignal measurements correspond to the same molecular tag sequence;determining a consensus base sequence based on the vector of consensusflow space signal measurements for the family; determining a consensussequence alignment by comparing the consensus base sequence to thesequence read having a highest mapping quality of the correspondingsequence alignments for the family; and generating a compressed datastructure comprising consensus compressed data, the consensus compresseddata including for each family, the consensus base sequence, theconsensus sequence alignment, the vector of consensus flow space signalmeasurements, the vector of standard deviations and the number ofmembers.
 17. The non-transitory machine-readable storage medium of claim16, further comprising instructions which cause the processor to performthe method, wherein the receiving further includes receiving at leastone model parameter corresponding to each vector of flow space signalmeasurements, wherein the method further comprises calculating anarithmetic mean of the model parameters of the corresponding vectors offlow space signal measurements of the family to form at least oneconsensus model parameter for the family, wherein the generating thecompressed data structure includes the consensus model parameters in theconsensus compressed data.
 18. The non-transitory machine-readablestorage medium of claim 16, further comprising instructions which causethe processor to perform the method, further comprising determining avariant in a given consensus base sequence using at least a portion ofthe consensus compressed data from the compressed data structure. 19.The non-transitory machine-readable storage medium of claim 16, furthercomprising instructions which cause the processor to perform the method,further comprising defining a subfamily of the family based on amatching flow synchronization, wherein the sequence reads of thesubfamily are flow synchronized.
 20. A system for compressing moleculartagged nucleic acid sequence data, comprising: a machine-readablememory; and a processor configured to execute machine-readableinstructions, which, when executed by the processor, cause the system toperform a method for compressing molecular tagged nucleic acid sequencedata, comprising: receiving a plurality of nucleic acid sequence reads,a plurality of vectors of flow space signal measurements and a pluralityof sequence alignments, wherein each sequence read is associated with amolecular tag sequence, the molecular tag sequence identifying a familyof sequence reads resulting from a particular polynucleotide molecule ina nucleic acid sample, wherein each vector of flow space signalmeasurements and each sequence alignment correspond with one of thesequence reads; grouping sequence reads associated with a same moleculartag sequence to form a family of sequence reads, corresponding vectorsof flow space signal measurements and corresponding sequence alignments,each family having a number of members; calculating an arithmetic meanof the corresponding vectors of flow space signal measurements to form avector of consensus flow space signal measurements for the family,wherein the vectors of flow space signal measurements correspond to thesame molecular tag sequence; calculating a standard deviation of thecorresponding vectors of flow space signal measurements to form a vectorof standard deviations for the family, wherein the vectors of flow spacesignal measurements correspond to the same molecular tag sequence;determining a consensus base sequence based on the vector of consensusflow space signal measurements for the family; determining a consensussequence alignment by comparing the consensus base sequence to thesequence read having a highest mapping quality of the correspondingsequence alignments for the family; and generating a compressed datastructure comprising consensus compressed data, the consensus compresseddata including for each family, the consensus base sequence, theconsensus sequence alignment, the vector of consensus flow space signalmeasurements, the vector of standard deviations and the number ofmembers.